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A discrete dwarf mongoose optimization algorithm to solve task assignment problems on smart farms

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Veröffentlicht in:Cluster Computing vol. 27, no. 5 (Aug 2024), p. 6185
1. Verfasser: Xu, Minzhi
Weitere Verfasser: Li, Weidong, Zhang, Xuejie, Su, Qian
Veröffentlicht:
Springer Nature B.V.
Schlagworte:
Mathematical programming
Scheduling
Heuristic
Computer centers
Task scheduling
Deep learning
Collaboration
Servers
Mathematical models
Cloud computing
Neural networks
Sensors
Optimization
Edge computing
Convex analysis
Approximation
Algorithms
Smart grid
Optimization algorithms
Energy consumption
Smart sensors
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Abstract:In this study, we propose a novel cloud-edge collaborative task assignment model for smart farms that consists of a cloud server, m edge servers, and n sensors. The edge servers rely solely on solar-generated energy, which is limited, whereas the cloud server has access to a limitless amount of energy supplied by the smart grid. Each entire task from a sensor is processed by either an edge server or the cloud server. We consider the task to be unsplittable. Building on the algorithm for the multimachine job scheduling problem, we develop a corresponding approximation algorithm. In addition, we propose a new discrete heuristic based on the dwarf mongoose optimization algorithmm, named the discrete dwarf mongoose optimization algorithm, and we utilize the proposed approximation algorithm to improve the convergence speed of this heuristic while yielding better solutions. In this study, we consider task sets with heavy tasks independently, where a heavy task is a task that requires many computing resources to process. If such tasks are assigned as ordinary tasks, the assignment results may be poor. Therefore, we propose a new method to solve this kind of problem.
ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-024-04271-3
Quelle:Advanced Technologies & Aerospace Database